A Generalized Vision-Based Intelligent Agent Navigation for Structural Damage Inspection

XU Yang; HU Shudong; YANG Guangshuo; BAO Yuequan; LI Hui

doi:10.3724/j.gyjzG25070801

Volume 55 Issue 7

Jul. 2025

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Article Navigation > INDUSTRIAL CONSTRUCTION > 2025 > 55(7): 1-10

XU Yang, HU Shudong, YANG Guangshuo, BAO Yuequan, LI Hui. A Generalized Vision-Based Intelligent Agent Navigation for Structural Damage Inspection[J]. INDUSTRIAL CONSTRUCTION, 2025, 55(7): 1-10. doi: 10.3724/j.gyjzG25070801

Citation:

XU Yang, HU Shudong, YANG Guangshuo, BAO Yuequan, LI Hui. A Generalized Vision-Based Intelligent Agent Navigation for Structural Damage Inspection[J]. INDUSTRIAL CONSTRUCTION, 2025, 55(7): 1-10. doi: 10.3724/j.gyjzG25070801

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A Generalized Vision-Based Intelligent Agent Navigation for Structural Damage Inspection

doi: 10.3724/j.gyjzG25070801

1. Key Lab of Smart Prevention and Mitigation of Civil Engineering Disasters of the Ministry of Industry and Information Technology, Harbin 150090, China;
2. School of Civil Engineering, Harbin Institute of Technology, Harbin 150090, China

Received Date: 2025-07-08
Available Online: 2025-09-12

Abstract

Abstract

Conventional structural inspection highly relies on human inspector with engineering experience， lacks safety guarantee， and is time-labor-consuming. Recently， advanced robotics， computer vision， and deep learning techniques have provided innovative solutions. This study proposed a generalized vision-based intelligent agent navigation for structural damage inspection， and established an interactive experimental environment for visual navigation agent integrating structural damage. A multi-modal information fusion network of optical image and depth information was designed， and a deep reinforcement learning network was developed based on the A3C algorithm with auxiliary tasks of collision prediction and reward prediction， in which a long-term memory module was embedded before the output layers of policy network and value network， and a decoupling module of value network was introduced based on universal successor representation. The model performance， effectiveness， and generalization ability were validated under various structural damage scenarios， and the results showed that it could achieve accurate mapping from observed images to navigation strategies and address conventional navigation limitations of lacking long-term memory and poor generalization ability in open environments.
- structural damage inspection,
- intelligent agent navigation,
- computer vision,
- deep reinforcement learning,
- multi-modal information fusion,
- auxiliary task enhancement

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References(23)

References

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